Enabling Multidisciplinary Perspective in Student Design Project: Fast Fashion and Sustainable Manufacturing Systems

Fast fashion retailers are growing faster than any other type of retailer due to their ability to offer trendy low-cost clothing mimicking latest runway trends with turnaround times as low as two weeks. Fueled by short production and distribution lead times, fast fashion retailers combine rapid prototyping, small batches of fashionable product designs, and efficient transportations and delivery. Among others, the methods applied in fast-fashion industry include mass customization and personalization, and lean manufacturing. Current trends in manufacturing lean towards the application of digital and rapid manufacturing methods and increased use of product lifecycle management, knowledge management systems and computer integrated manufacturing. Furthermore, modern fashion systems span geographical regions, wherein design and manufacturing is not necessarily done at the same location and it requires coordination of many pairs of hands and machines, followed by multiple processes and treatments to meet the demands of ever decreasing time-to-market. Hence, there are connections that can be used as a benefit for multidisciplinary student projects which would include fashion merchandising students and engineering students. Therefore, the purpose of this paper is to present a model of a project which would include a team of students with diverse backgrounds and experiences in fashion, engineering, and industrial technology in order to examine various manufacturing system concepts that can be used to enhance the sustainability of fast-fashion systems. These activities would be embedded in their current courses and they would expose engineering students to a fashion manufacturing industry and fashion students to engineering concepts of product lifecycle management and computer aided manufacturing. Special emphasis would be given to female engineering students who are not necessarily exposed to this kind of industry in their major

SIMULATION OF BREED AND CROSSBREEDING EFFECTS ON COSTS OF PORK PRODUCTION

A bio-economic model of swine production was used to simulate expected performance effects of breeds in alternative breeding systems on total costs/100 kg of live weight (EWW) or/l00 kg lean (ELW) for marketing at 100 kg live weight and on costs/100 kg lean for marketing at mean 185-d weight (ELA). Effects of heterosis and of six U.S. breeds were simulated for integrated industry purebred (P), two-breed specific (2S), backcross (2B) and rotation cross (2R), and three-breed specific (3S) and rotation cross (3R) breeding systems. Traits considered were age at puberty (-PUB), conception rate (CR), litter size born alive (NBA), preweaning viability (VIAB), milk production (MILK), age at 100 kg live weight (-DAYS) and empty body fat percentage (-FAT). Cost reductions from crossbreeding systems were greater for ELA than for ELW or EWW, ranging from -3 to -5% for 2S, -6 to -7% for 2B and 2R, and -7 to -9% for 3S and 3R. Reductions in nonfeed costs were much greater than those in feed costs for EWW and ELW (-4 to -12% vs -2 to -4%), and especially for ELA (-9 to -17% vs -1 to -2%). Order of maternal trait importance in ranking breeds was NBA, VIAB, CR, MILK and -PUB for P, 2R and 3R systems and as maternal breeds in 2S and 3S systems. For cost of lean, -FAT was as important as NBA in all except maternal breed roles. For ELA, -DAYS was important in all breed roles, but not for EWW and ELW, especially in maternal breed roles. In ranking paternal breeds for use in 2S and 3S systems, the important traits were only VIAB for EWW, VIAB and -FAT for ELW, but VIAB,-FAT and -DAYS for ELA. Existing breeds ranked differently as paternal breeds than as maternal or general purpose breeds. Complementary paternal-maternal effects permitted greater cost reductions from best 3S (-7 to -10%) than from best 3R (-6 to -8%) breed combinations. Maternal breeds in crosses benefited from superiority in components of both sow and pig performance

Three dimensional graphics station for computer integrated manufacturing research

Issued as Final report, Project no. E-25-69

Accuracy of predictive methods to estimate resting energy expenditure of thermally-injured patients

Background The purpose of this study was to evaluate the bias and precision of 46 methods published from 1953 to 2000 for estimating resting energy expenditure (REE) of thermally injured patients. Methods Twenty-four adult patients with ≥20% body surface area burn admitted to a burn center who required specialized nutrition support and who had their REE measured via indirect calorimetry (IC) were evaluated. Patients with morbid obesity, human immunovirus, malignancy, pregnancy, hepatic or renal failure, neuromuscular paralysis, or those requiring a FiO2 \u3e50% or positive end expiratory pressure (PEEP) ≥10 cm H2O were excluded. One steady-state measured REE measurement (MEE) was obtained per patient. The methods of Sheiner and Beal were used to assess bias and precision of these methods. The formulas were considered unbiased if the 95% confidence interval (CI) for the error (kilocalories per day) intersected 0 and were considered precise if the 95% CI for the absolute error (%) was within 15% of MEE. Results MEE was 2780 ± 567 kcal/d or 158% ± 34% of the Harris Benedict equations. None of the methods was precise (≤15% CI error). Over one-half (57%) of the 46 methods had a 95% confidence interval error \u3e30% of the MEE. Forty-eight percent of the methods were unbiased, 33% were biased toward overpredicting MEE, and 19% consistently underpredicted MEE. The pre-1980s methods more frequently overpredicted MEE compared with the 1990 to 2000 (p \u3c .01) and 1980 to 1989 (p \u3c .05) published methods, respectively. The most precise unbiased methods for estimating MEE were those of Milner (1994) at a mean error of 16% (CI of 10% to 22%), Zawacki (1970) with a mean error of 16% (CI of 9% to 23%), and Xie (1993) at a mean error of 18% (CI of 12% to 24%). The conventional 1.5 times the Harris Benedict equations was also unbiased and had a mean error of 19% (CI of 9% to 29%). Conclusions Thermally injured patients are variably hypermetabolic and energy expenditure cannot be precisely predicted. If IC is not available, the most precise, unbiased methods were those of Milner (1994), Zawacki (1970), and Xie (1993)